Fix spelling errors

docs/final-report/report.tex

@@ -23,36 +23,36 @@
 % To be edited - my (Lukas) suggestion so far
 \section{Project Description}
 
-    \subsection{Converstaional AI and Training}
+    \subsection{Conversational AI and Training}
     Conversational AI describes computer systems that users can interact with by having a
     conversation. One important goal is to make the conversation seem as natural as possible.
-    Ideally, an interacting user should assume to be interacting with another human beeing. This
-    can make communication with a computer become very pleasant and easy for humaning beeings as
-    they are simply using the language the always use. Besides there is no need for menu
-    interaction with the system and thus no learning curve required.
+    Ideally, an interacting user should assume to be interacting with another human. This
+    can make communication with a computer become very pleasant and easy for humans as
+    they are simply using their natural language. Besides there is no need for menu
+    interaction with the system and thus no learning curve.
     % TODO add example use case (website information)
     % TODO add more benefits (24/7 availability)
     \\ Conversational AI can be used in Voice Assistants that communicate through spoken words or
-    through chatbots that imitate a human beeing one is chatting with by text messages.
+    through chatbots that imitate a human by sending text messages.
 
-    \subsection{Rasa Framwork}
-    Rasa is a collection of frameworks for conversational AI software. The Rasa Stack contains two
+    \subsection{Rasa Framework}
+    Rasa is a collection of tools for conversational AI software. The Rasa Stack contains two
     open source libraries called Rasa NLU and Rasa Core that can be used to create contextual
     chatbots. Rasa NLU is a library for natural language understanding with intent classification
-    and entity extraction Rasa Core is a Chatbot framework with machine learning based dialogue
+    and entity extraction Rasa Core is a chatbot framework with machine learning based dialogue
     management. Both can be uses independently but rasa recommends using both.
     % TODO add description of how a rasa bot must be trained to achieve results
 
     \subsection{Research Question}
     The objective of this project is to find out, wether chatbots can be trained with natural
-    language texts \textit{automatically}. There are two inital research questions: Given that
-    chatbots need to be trained with knowledge, called facts.
+    language texts \textit{automatically}. There are two initial research questions: Given that
+    chatbots need to be trained with knowledge, called facts:
     \begin{itemize}
-        \item Can these facts be extracted from natural language text?
-        \item Can this be done automaitcally? 
+        \item can these facts be extracted from natural language text?
+        \item can this be done automatically? 
     \end{itemize}
     
-\section{Solution Approach}
+\section{Approach}
     \subsection{Project Goals}
     \subsection{Rasa Setup and Intents}
     \subsection{Scrapping of Source Texts}
@@ -61,11 +61,11 @@
 \section{Software Architecture}
 
     \subsection{Rasa Chatbot}
-    The Rasa Chatbot built for this project uses both Rasa Stack components - \textit{Rasa Core}
-    and \textit{Rasa NLU}. Configuration has been organised in reference to examples from the Rasa
-    github repository. \\ Rasa NLU has been trained with example questions in Markdown format that
-    contain highlighted enities. This ensures that the bot to understand intents and extract the
-    entities inside the sentences. One example can be seen in listing \ref{nlu_example}. \\
+    The chatbot built for this project uses both Rasa Stack components - \textit{Rasa Core}
+    and \textit{Rasa NLU}. Configuration has been organized in reference to examples from the Rasa
+    github repository. \\ Rasa NLU has been trained with example questions in markdown format that
+    contain highlighted entities. This ensures that the bot is able to understand intents and
+    extract the entities inside the sentences. One example can be seen in listing \ref{nlu_example}. \\
 
     \lstinputlisting[label={nlu_example}, caption={NLU example}]{nlu_example.md}
 
@@ -74,7 +74,7 @@
     contains all actions, entities, slots, intents, and templates the bot deals with. \textit
     {Templates} means template strings for bot utterances. \textit{Slots} are variables that can
     hold different values. The bot proposed in this project uses a slot to store the name of a
-    recognized physicist entity for instance. According to the Rasa website
+    recognized physicist entity. According to the Rasa website
     \footnote{\url{https://rasa.com/docs/get_started_step2/}}
     , the domain is \textit{the universe the bot is living in}. \\
 
@@ -90,22 +90,25 @@
     conversation ability available.
 
     \begin{center}
-        \begin{tabular}{| c | l | l |}
-            \hline
-            No & Intent & Example \\ \hline
-            1 & birthdate & When was Albert Einstein born \\ \hline
-            2 & nationality & Where was Albert Einstein born \\ \hline
-            3 & day of death & When did Albert Einstein die \\ \hline
-            4 & place of death & Where did Albert Einstein die \\ \hline
-            5 & is alive & Is Albert Einstein still alive \\ \hline
-            6 & spouse & Who was Albert Einstein married to \\ \hline
-            7 & primary education & Where did Albert Einstein go to school \\ \hline
-            8 & university & Which university did Albert Einstein attend \\ \hline
-            9 & area of research & What was Albert Einstein area of research \\ \hline
-            10 & workplace & Where did Albert Einstein work \\ \hline
-            11 & awards & What awards did Albert Einstein win \\ \hline
-        \end{tabular}
-        \label{table:intent_table}
+        \begin{table}
+            \begin{tabular}{| c | l | l |}
+                \hline
+                No & Intent & Example \\ \hline
+                1 & birthdate & When was Albert Einstein born \\ \hline
+                2 & nationality & Where was Albert Einstein born \\ \hline
+                3 & day of death & When did Albert Einstein die \\ \hline
+                4 & place of death & Where did Albert Einstein die \\ \hline
+                5 & is alive & Is Albert Einstein still alive \\ \hline
+                6 & spouse & Who was Albert Einstein married to \\ \hline
+                7 & primary education & Where did Albert Einstein go to school \\ \hline
+                8 & university & Which university did Albert Einstein attend \\ \hline
+                9 & area of research & What was Albert Einstein area of research \\ \hline
+                10 & workplace & Where did Albert Einstein work \\ \hline
+                11 & awards & What awards did Albert Einstein win \\ \hline
+            \end{tabular}
+            \caption{Intents that are recognized by the bot}
+            \label{table:intent_table}
+        \end{table}
     \end{center}
 
     \subsection{R Package 'wikiproc'}